High-Order Steady-State Diffusion Approximations-Reference-Cited by-同舟云学术

High-Order Steady-State Diffusion Approximations

Published:2022-09-08 Issue: Volume: Page:
ISSN:0030-364X
Container-title:Operations Research
language:en
Short-container-title:Operations Research

Author:

Braverman Anton¹^ORCID,Dai J. G.²³^ORCID,Fang Xiao⁴

Affiliation:

1. Kellogg School of Management, Northwestern University, Evanston, Illinois 60201;

2. School of Operations Research and Information Engineering, Cornell University, Ithaca, New York 14853;

3. School of Data Science and Shenzhen Research Institute of Big Data, The Chinese University of Hong, Shenzhen, Longgang District 518172, People's Republic of China;

4. Department of Statistics, The Chinese University of Hong Kong, Hong Kong

Abstract

Much like higher-order Taylor expansions allow one to approximate functions to a higher degree of accuracy, we demonstrate that, by accounting for higher-order terms in the Taylor expansion of a Markov process generator, one can derive novel diffusion approximations that achieve a higher degree of accuracy compared with the classical ones used in the literature over the last 50 years.

Publisher

Institute for Operations Research and the Management Sciences (INFORMS)

Subject

Management Science and Operations Research,Computer Science Applications

Link

http://pubsonline.informs.org/doi/pdf/10.1287/opre.2022.2362

Reference42 articles.

1. Stein's method and poisson process convergence

2. The Prelimit Generator Comparison Approach of Stein’s Method

3. Stein’s method for steady-state diffusion approximations of $M/\mathit{Ph}/n+M$ systems

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